checkpoint_config = dict(interval=1, max_keep_ckpts=3, save_last=True)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
pretrained = 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_t_1k_224.pth'
checkpoint = '/mnt/pde/algorithm/user/qxu/data/ckpt/InternImg/dino_4scale_internimage_t_1x_coco.pth'
backbone = dict(
    type='InternImage',
    core_op='DCNv3',
    channels=64,
    depths=[4, 4, 18, 4],
    groups=[4, 8, 16, 32],
    mlp_ratio=4.0,
    drop_path_rate=0.2,
    norm_layer='LN',
    layer_scale=1.0,
    offset_scale=1.0,
    post_norm=False,
    with_cp=True,
    out_indices=(1, 2, 3),
    init_cfg=dict(
        type='Pretrained',
        checkpoint=
        'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_t_1k_224.pth'
    ))
task = ['cls', 'detection', 'segmention']
cls_model = dict(
    num_classes=1000,
    aug=dict(mixup=0.0, cutmix=0.0, reprob=0.0),
    loss=dict(label_smoothing=0.3))
detection_model = dict(
    neck=dict(
        in_channels=[128, 256, 512],
        kernel_size=1,
        out_channels=256,
        act_cfg=None,
        norm_cfg=dict(type='GN', num_groups=32),
        num_outs=4),
    bbox_head=dict(
        type='DINOHead',
        num_query=900,
        num_classes=80,
        in_channels=2048,
        sync_cls_avg_factor=True,
        as_two_stage=True,
        with_box_refine=True,
        dn_cfg=dict(
            type='CdnQueryGenerator',
            noise_scale=dict(label=0.5, box=1.0),
            group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=100)),
        transformer=dict(
            type='DinoTransformer',
            two_stage_num_proposals=900,
            encoder=dict(
                type='DetrTransformerEncoder',
                num_layers=6,
                transformerlayers=dict(
                    type='BaseTransformerLayer',
                    attn_cfgs=dict(
                        type='MultiScaleDeformableAttention',
                        embed_dims=256,
                        dropout=0.0),
                    feedforward_channels=2048,
                    ffn_dropout=0.0,
                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
            decoder=dict(
                type='DinoTransformerDecoder',
                num_layers=6,
                return_intermediate=True,
                transformerlayers=dict(
                    type='DetrTransformerDecoderLayer',
                    attn_cfgs=[
                        dict(
                            type='MultiheadAttention',
                            embed_dims=256,
                            num_heads=8,
                            dropout=0.0),
                        dict(
                            type='MultiScaleDeformableAttention',
                            embed_dims=256,
                            dropout=0.0)
                    ],
                    feedforward_channels=2048,
                    ffn_dropout=0.0,
                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
                                     'ffn', 'norm')))),
        positional_encoding=dict(
            type='SinePositionalEncoding',
            num_feats=128,
            temperature=20,
            normalize=True),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
    train_cfg=dict(
        assigner=dict(
            type='HungarianAssigner',
            cls_cost=dict(type='FocalLossCost', weight=2.0),
            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
    test_cfg=dict(max_per_img=100))
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = dict(
    detection=[
        dict(type='LoadImageFromFile'),
        dict(type='LoadAnnotations', with_bbox=True),
        dict(type='RandomFlip', flip_ratio=0.5),
        dict(
            type='AutoAugment',
            policies=[[{
                'type':
                'Resize',
                'img_scale': [
                    (480, 1333), (512, 1333), (544, 1333), (576, 1333),
                    (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                    (736, 1333), (768, 1333), (800, 1333)
                ],
                'multiscale_mode':
                'value',
                'keep_ratio':
                True
            }],
                      [{
                          'type': 'Resize',
                          'img_scale': [(400, 4200), (500, 4200), (600, 4200)],
                          'multiscale_mode': 'value',
                          'keep_ratio': True
                      }, {
                          'type': 'RandomCrop',
                          'crop_type': 'absolute_range',
                          'crop_size': (384, 600),
                          'allow_negative_crop': False
                      }, {
                          'type':
                          'Resize',
                          'img_scale': [(480, 1333), (512, 1333), (544, 1333),
                                        (576, 1333), (608, 1333), (640, 1333),
                                        (672, 1333), (704, 1333), (736, 1333),
                                        (768, 1333), (800, 1333)],
                          'multiscale_mode':
                          'value',
                          'override':
                          True,
                          'keep_ratio':
                          True
                      }]]),
        dict(
            type='Normalize',
            mean=[123.675, 116.28, 103.53],
            std=[58.395, 57.12, 57.375],
            to_rgb=True),
        dict(type='Pad', size_divisor=32),
        dict(type='DefaultFormatBundle'),
        dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
    ],
    cls=None)
test_pipeline = dict(
    detection=[
        dict(type='LoadImageFromFile'),
        dict(
            type='MultiScaleFlipAug',
            img_scale=(1333, 800),
            flip=False,
            transforms=[
                dict(type='Resize', keep_ratio=True),
                dict(type='RandomFlip'),
                dict(
                    type='Normalize',
                    mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True),
                dict(type='Pad', size_divisor=32),
                dict(type='ImageToTensor', keys=['img']),
                dict(type='Collect', keys=['img'])
            ])
    ],
    cls=None)
data = dict(
    detection=dict(
        samples_per_gpu=2,
        workers_per_gpu=1,
        train=dict(
            type='CocoDataset',
            ann_file=
            '/mnt/pde/algorithm/user/qxu/data/detection/coco2017/annotations/instances_train2017.json',
            img_prefix=
            '/mnt/pde/algorithm/user/qxu/data/detection/coco2017/train2017/',
            pipeline=[
                dict(type='LoadImageFromFile'),
                dict(type='LoadAnnotations', with_bbox=True),
                dict(type='RandomFlip', flip_ratio=0.5),
                dict(
                    type='AutoAugment',
                    policies=[[{
                        'type':
                        'Resize',
                        'img_scale': [(480, 1333), (512, 1333), (544, 1333),
                                      (576, 1333), (608, 1333), (640, 1333),
                                      (672, 1333), (704, 1333), (736, 1333),
                                      (768, 1333), (800, 1333)],
                        'multiscale_mode':
                        'value',
                        'keep_ratio':
                        True
                    }],
                              [{
                                  'type':
                                  'Resize',
                                  'img_scale': [(400, 4200), (500, 4200),
                                                (600, 4200)],
                                  'multiscale_mode':
                                  'value',
                                  'keep_ratio':
                                  True
                              }, {
                                  'type': 'RandomCrop',
                                  'crop_type': 'absolute_range',
                                  'crop_size': (384, 600),
                                  'allow_negative_crop': False
                              }, {
                                  'type':
                                  'Resize',
                                  'img_scale': [(480, 1333), (512, 1333),
                                                (544, 1333), (576, 1333),
                                                (608, 1333), (640, 1333),
                                                (672, 1333), (704, 1333),
                                                (736, 1333), (768, 1333),
                                                (800, 1333)],
                                  'multiscale_mode':
                                  'value',
                                  'override':
                                  True,
                                  'keep_ratio':
                                  True
                              }]]),
                dict(
                    type='Normalize',
                    mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True),
                dict(type='Pad', size_divisor=32),
                dict(type='DefaultFormatBundle'),
                dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
            ]),
        val=dict(
            type='CocoDataset',
            ann_file=
            '/mnt/pde/algorithm/user/qxu/data/detection/coco2017/annotations/instances_val2017.json',
            img_prefix=
            '/mnt/pde/algorithm/user/qxu/data/detection/coco2017/val2017/',
            pipeline=[
                dict(type='LoadImageFromFile'),
                dict(
                    type='MultiScaleFlipAug',
                    img_scale=(1333, 800),
                    flip=False,
                    transforms=[
                        dict(type='Resize', keep_ratio=True),
                        dict(type='RandomFlip'),
                        dict(
                            type='Normalize',
                            mean=[123.675, 116.28, 103.53],
                            std=[58.395, 57.12, 57.375],
                            to_rgb=True),
                        dict(type='Pad', size_divisor=32),
                        dict(type='ImageToTensor', keys=['img']),
                        dict(type='Collect', keys=['img'])
                    ])
            ]),
        test=dict(
            type='CocoDataset',
            ann_file=
            '/mnt/pde/algorithm/user/qxu/data/detection/coco2017/annotations/instances_val2017.json',
            img_prefix=
            '/mnt/pde/algorithm/user/qxu/data/detection/coco2017/val2017/',
            pipeline=[
                dict(type='LoadImageFromFile'),
                dict(
                    type='MultiScaleFlipAug',
                    img_scale=(1333, 800),
                    flip=False,
                    transforms=[
                        dict(type='Resize', keep_ratio=True),
                        dict(type='RandomFlip'),
                        dict(
                            type='Normalize',
                            mean=[123.675, 116.28, 103.53],
                            std=[58.395, 57.12, 57.375],
                            to_rgb=True),
                        dict(type='Pad', size_divisor=32),
                        dict(type='ImageToTensor', keys=['img']),
                        dict(type='Collect', keys=['img'])
                    ])
            ])),
    cls=None)
optimizer = dict(
    type='AdamW',
    lr=0.0001,
    weight_decay=0.0001,
    constructor='CustomLayerDecayOptimizerConstructor',
    paramwise_cfg=dict(
        num_layers=30, layer_decay_rate=0.9, depths=[4, 4, 18, 4]))
optimizer_config = dict(
    _delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(
    detection=dict(
        policy='step',
        warmup='linear',
        warmup_iters=500,
        warmup_ratio=0.001,
        step=[11]),
    cls=dict(warmup_iters=500))
total_epoch = 12
evaluation = dict(
    detection=dict(interval=1, metric='bbox', classwise=True), cls=None)
dataset_type = dict(detection='CocoDataset', cls='Imagenet')
data_root = dict(
    detection='/mnt/pde/algorithm/user/qxu/data/detection/coco2017/',
    cls='/mnt/pde/algorithm/user/qxu/data/cls')
LOCAL_RANK = -1
WORLD_SIZE = -1
DISTRIBUTED = False
auto_scale_lr = True
base_batch_size = 2
work_dir = './work_dirs/all_in_one'
auto_resume = False
gpu_ids = range(0, 1)
